Animal acoustic communication often takes the form of complex sequences, made up of multiple distinct acoustic units. Apart from the well-known example of birdsong, other animals such as insects, amphibians,and mammals ...

A key enabler of pervasive computing is the ability to drive service delivery through the analysis of situations: Semantically meaningful classifications of system state, identified through analysing the readings from ...

Autonomously detecting and recovering from faults is one approach for reducing the operational complexity and costs associated with managing computing environments. We present a novel methodology for autonomously generating ...

Autonomously detecting and recovering from faults is one approach for reducing the operational complexity and costs associated with managing computing environments. We present a novel methodology for autonomously generating ...

One of the initial steps of modern drug discovery is the identification of small organic molecules able to inhibit a target macromolecule of therapeutic interest. A small proportion of these hits are further developed into ...

Solubility prediction usually refers to prediction of the intrinsic aqueous solubility, which is the concentration of an unionised molecule in a saturated aqueous solution at thermodynamic equilibrium at a given temperature. ...

Backtracking CSP solvers provide a powerful framework for search and reasoning.
The aim of constraint learning is increase global reasoning power by learning
new constraints to boost reasoning and hopefully reduce search ...

Systems pathology attempts to introduce more holistic approaches
towards pathology and attempts to integrate clinicopathological
information with “-omics” technology. This doctorate researches
two examples of a systems ...

Machine learning has been a source for continuous methodological advances in the field of computational learning from data. Systems biology has profited in various ways
from machine learning techniques but in particular ...

Machine learning algorithms are generally developed in computer science or adjacent disciplines and find their way into chemical modeling by a process of diffusion. Though particular machine learning methods are popular ...

The Algorithm Selection Problem is to select the most appropriate way for solving a problem given a choice of different ways. Some of the most prominent and successful applications come from Artificial Intelligence and in ...

In this work we make predictions of several important molecular properties of academic and industrial importance to seek answers to two questions: 1) Can we apply efficient machine learning techniques, using inexpensive ...

Background: The World Anti-Doping Agency (WADA) publishes the Prohibited List, a manually compiled international standard of substances and methods prohibited in-competition, out-of-competition and in particular sports. ...

The most widely used classification system describing enzyme-catalysed reactions
is the Enzyme Commission (EC) number. Understanding enzyme
function is important for both fundamental scientific and pharmaceutical
reasons. ...

In RadarCat we present a small, versatile radar-based system for material and object classification which enables new forms of everyday proximate interaction with digital devices. We demonstrate that we can train and ...

Search behavior is often used as a proxy for foraging effort within studies of animal movement, despite it being only one part of the foraging process, which also includes prey capture. While methods for validating prey ...

Rising complexity within multi-tier computing architectures remains an open problem. As complexity increases, so do the costs associated with operating and maintaining systems within these environments. One approach for ...

Wireless sensor networks usually operate in dynamic, stochastic environments. While the behaviour of individual nodes is important, they are better seen as contributors to a larger mission, and managing the sensing quality ...